Multi-agent-systems

This post explores the X-MAS framework, which investigates the benefits of using diverse Large Language Models (LLMs) within multi-agent systems (MAS). It details X-MAS-Bench, a comprehensive testbed evaluating 27 LLMs across 5 domains and 5 MAS functions, revealing that no single LLM excels universally. Building on these findings, the paper demonstrates significant performance improvements (up to 47-63% on challenging math problems) when transitioning homogeneous MAS to heterogeneous configurations, highlighting the potential of leveraging collective intelligence from diverse LLMs.